Searching for Explanations for the Gender Gap in STEM: Methodological - - PowerPoint PPT Presentation

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Searching for Explanations for the Gender Gap in STEM: Methodological - - PowerPoint PPT Presentation

Aug 12, 2023 215 likes •387 views

Searching for Explanations for the Gender Gap in STEM: Methodological Approaches from across K-16 Presentations Linda J. Sax, UCLA Beth Casey, Boston College Karen Kim, CSU Fullerton Breakout groups 1. National datasets 2. Intervention

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Presentations

Linda J. Sax, UCLA Beth Casey, Boston College Karen Kim, CSU Fullerton

Breakout groups

  • 1. National datasets
  • 2. Intervention studies
  • 3. Qualitative research

Searching for Explanations for the Gender Gap in STEM: Methodological Approaches from across K-16

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Using the Freshman Survey to Study the Gender Gap in STEM

(or, Like Being a Kid in a Candy Shop)

Linda J. Sax, UCLA

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Women Overrepresented Across All Fields, but Underrepresented in STEM

57 35 43 65

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

All Bachelor's Degree Recipients STEM Bachelor's Degree Recipients

Proportions of Bachelor’s Degree Recipients, by Gender

Women Men

Source: National Center for Education Statistics, Digest of Education Statistics, 2011

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Women’s Relative Representation in STEM Varies by Field

17 18 41 43 58 83 82 59 57 42 0% 20% 40% 60% 80% 100%

Engineering Computer Science Physical Sciences Mathematics/Statistics Biological Sciences

Proportions of Bachelor’s Degree Recipients, by Gender

Women Men

Source: National Center for Education Statistics, Digest of Education Statistics, 2011

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NSF Research Part I: Disaggregating the Gender Gap in STEM

(NSF HRD #1135727)

How has the gender gap in aspirations to major in different STEM majors changed over time?

Freshman Survey

  • Nation’s largest and longest-

running study of entering college students, housed at UCLA’s HERI

  • Between 1971 and 2011,

responses from over 8 million students entering over 1,000 baccalaureate institutions

  • Study focuses on 5 STEM fields
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SLIDE 6

Engineering

Stable(ish) Gender Gap

0% 5% 10% 15% 20% 25% 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Men Women

Source: Cooperative Institutional Research Program Freshman Survey, Higher Education Research Institute, UCLA

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Computer Science

Fluctuating Gender Gap

0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15% 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Men

Women

Source: Cooperative Institutional Research Program Freshman Survey, Higher Education Research Institute, UCLA

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Physical Sciences

Diminished Gender Gap

0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15% 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Men Women

Source: Cooperative Institutional Research Program Freshman Survey, Higher Education Research Institute, UCLA

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Math/Statistics

No Gender Gap

0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15% 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Men Women

Source: Cooperative Institutional Research Program Freshman Survey, Higher Education Research Institute, UCLA

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Biological Sciences

Gender Gap Reversal

0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15% 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Men Women

Source: Cooperative Institutional Research Program Freshman Survey, Higher Education Research Institute, UCLA

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How have the determinants of men’s and women’s STEM major selection changed over time? Example from computer science…

  • Data from 1976, 1986, 1996, 2006, and 2011
  • 18,830 first-year “intended” computer science majors
  • 904,307 first-year students from all other majors
  • Logistic regressions run separately by gender
  • Dependent variable: Intent to major in CS
  • Independent variables blocked according to Social Cognitive

Career Theory

  • Changing salience examined through “Variable x Time”

interactions

NSF Research Part II: Evolving Predictors of STEM Major Selection

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SLIDE 12

Positive Men Women Father’s Career in STEM

+ +

Asian/Asian-American

+ +

African American

+ +

Status Striving Orientation

+ +

Negative Men Women Social Activist Orientation

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BOTH GENDERS Positive predictors Math Self-Ratings Weaker positive Scholarly Orientation Stronger positive Negative predictors Family Income Weaker negative Leadership Orientation Stronger negative Goal: Raising a family Stronger negative WOMEN ONLY Scientific Orientation Stronger positive Artistic Orientation Weaker negative

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NSF Research Part III: Decomposition Analysis

What are the roots of the gender gap over time? Decomposition Analysis

  • Used logistic regression and mean replacement to identify the extent

to which gender differences in CS major choice can be attributed to:

  • differences in average characteristics between men and

women

  • differences in the salience of those characteristics

 Conducted for 1976, 1986, 1996, 2006, 2011

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Proportions of the Computer Science Gender Gap Explained by Characteristics versus Coefficients Source of Gender Gap 1976 (%) 1986 (%) 1996 (%) 2006 (%) 2011 (%) Gender Differences in Characteristics 78.5 44.1 39.1 35.5 34.9 Gender Differences in Coefficients 21.5 55.9 60.9 64.5 65.1

What Matters Most in Explaining the CS Gender Gap?

Bottom line: The gender gap is increasingly explained by gender differences in the salience of variables, rather than the mean values of the variables.

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 Sample

 Upside: With 8 million students, possibilities for disaggregation are endless (unique STEM disciplines, institutional type, student demographics, etc.)  Downside: Ditto!

 Variables

 Upside: Large number of variables with some consistency

  • ver 40 years

 Downside: Not 100% consistency over time (limitations in available variables); available variables not a perfect fit for theoretical framework

 Other

 Major aspiration ≠ degree earned

Methodological Considerations